Circulating Levels of Osteoprotegerin, Osteocalcin and Osteopontin in Patients with Rheumatoid Arthritis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: Currently published data regarding the potential role of osteoprotegerin (OPG), osteocalcin (OCN) and osteopontin (OPN) for the discrimination between rheumatoid arthritis (RA) and osteoarthritis (OA) are contradictory. To derive a more precise evaluation, a meta-analysis was performed. Methods: Published literatures comparing plasma/serum OPG, OCN and OPN levels between RA group and OA controls were searched in PubMed, Embase and the Cochrane Library. The Newcastle-Ottawa Scale was used to assess the study quality. Pooled standard mean difference (SMD) with 95% confidence interval (CI) was calculated by random-effect model analysis. Heterogeneity test was performed by the Q statistic and quantified using I2. Results: Nine studies including 438 RA patients and 255 OA patients were finally incorporated in the meta-analysis after examining title, type, abstracts and full text. The results showed that RA patients had higher plasma/serum OPN (pooled SMD = −2.57, 95% CI = −4.72 to −0.41) levels when compared to OA patients. No significant difference in plasma/serum OPG (pooled SMD = −0.29, 95% CI = −1.07‒0.49) and OCN (pooled SMD = −0.09, 95% CI = −0.48‒0.31) levels were found between RA patients and OA patients. Subgroup analysis indicated that plasma/serum OPG levels had no significant differences between RA patients and OA patients in Europe and Asian. Conclusions: Overall, there is no significant difference in circulating OPG and OCN levels between RA patients and OA patients. However, plasma/serum OPN level is significantly higher in RA patients compared with OA patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".